Evidence-based practices : reading comprehension instruction and teacher self-efficacy

Despite growing evidence informing educators for effective reading comprehension (RC) teaching, it is unclear how extensively this evidence base is implemented in practice and teacher self-efficacy can have a role to play. Recent estimates suggest about 15% of U.K. students are below expected levels of attainment by the end of KS2. While Educational Psychologists can support the implementation of evidence base in classrooms to raise literacy standards, there is a lack of U.K. studies that examined the extent that the evidence base in RC instruction is implemented in practice and how confident teachers feel about implementing them.\ud \ud The aims of this study were three-fold. Firstly, to explore the extent that U.K. teachers used evidence-based practices when teaching RC. Secondly, to investigate teacher confidence levels in using them. Lastly, to examine the relationship between teacher self-efficacy and the extent they used evidence-based RC instructional practices. A mixed methods non-experimental fixed research design was used. The questionnaire sent out to 379 KS2 teachers resulted in 29 responses (i.e. 7.7% response rate). A subset of nine teachers participated in the systematic classroom observation of their RC lessons and a further subset of three teachers participated as case studies in follow up interviews.\ud \ud The results indicated that some evidence-based practices were incorporated in KS2 RC instruction and teachers generally felt confident about their implementation. However, the range of evidence-based practices used was limited and it was not always practices with the strongest evidence base. A positive relationship between teacher self-efficacy and the extent that teachers incorporated evidence-based RC instructional practices was observed, although this relationship appeared complex. The findings from this exploratory study contributed towards the existing gap in research on implementation of evidence based teaching practice for RC instruction in U.K. schools. Implications for professional practice and recommendations for further research are considered

Electrical instability of amorphous indium-gallium-zinc oxide thin film transistors under monochromatic light illumination

The electrical instability behaviors of a positive-gate-bias-stressed amorphous indium-gallium-zinc oxide (a-IGZO) thin film transistor(TFT) are studied under monochromatic light illumination. It is found that as the wavelength of incident light reduces from 750 nm to 450 nm, the threshold voltage of the illuminated TFT shows a continuous negative shift, which is caused by photo-excitation of trapped electrons at the channel/dielectric interface. Meanwhile, an increase of the sub-threshold swing (SS) is observed when the illumination wavelength is below 625 nm (∼2.0 eV). The SS degradation is accompanied by a simultaneous increase of the field effect mobility (μFE) of the TFT, which then decreases at even shorter wavelength beyond 540 nm (∼2.3 eV). The variation of SS and μFE is explained by a physical model based on generation of singly ionized oxygen vacancies (Vo⁺) and double ionized oxygen vacancies (Vo²⁺) within the a-IGZO active layer by high energy photons, which would form trap states near the mid-gap and the conduction band edge, respectively.This work was supported by the State Key Program for Basic Research of China under Grant Nos. 2010CB327504, 2011CB922100, 2011CB301900; the National Natural Science Foundation of China under Grant Nos. 60825401, 60936004, 11104130, BK2011556, and BK2011050

A retrospective research on non-suicidal self-injurious behaviors among young patients diagnosed with mood disorders

BackgroundNon-suicidal self-injury (NSSI) is an emerging public concern in both clinical and non-clinical settings, especially in the background of the coronavirus disease 2019 (COVID-19) pandemic. Nevertheless, knowledge of NSSI on a certain disease entity in the later stage of the pandemic was scarce.ObjectiveThis study was conducted for the purpose of exploring the current occurrence and characteristics of NSSI in patients diagnosed with mood disorders (MDs) as well as its correlated factors in the later stage of the pandemic.MethodsThree hundred and forty-nine eligible subjects (M ± SD, 21.54 ± 7.62) admitted to a mental health center in Wuhan from 11 November 2021 to 31 January 2022 were included in our study. An umbrella questionnaire comprised of demographics, COVID-19-related factors, Yale-Brown Obsessive and Compulsive Scale (Y-BOCS), Pittsburgh Sleep Quality Index-Revised (PSQI-R), Mobile Phone Addiction Index (MPAI), and Ottawa Self-injury Inventory (OSI) was extended to each subject via shared QR code.ResultsOf 349 patients with MDs included, 151 (43.27%) reported NSSI in the recent 1 month, among whom hand, lower arm/wrist, and scalp were the most hurt body parts, and cutting, hitting, and headbanging were the most adopted methods. “Own idea” was the most common origin of NSSI. In the logistic regression model, age bracket, family monthly income, occupation, level of obsessive-compulsive disorder (OCD) symptoms, sleep duration, withdrawal reaction to the mobile phone, and habits of using a mobile phone were independently associated with NSSI.ConclusionIt was revealed by our study that NSSI was quite prevalent among patients with MDs, especially among those students, adolescents, comorbid with OCD symptoms, inadequate sleeping hours, and suffering from withdrawal reaction to mobile phones. Further research on NSSI in various psychiatric disorders and even in non-clinical settings such as the community population was in urgent need since NSSI in China was not rare

A New Procedure for Treating a Sebaceous Cyst: Removal of the Cyst Content with a Laser Punch and the Cyst Wall with a Minimal Postponed Excision

Three main techniques are used to excise sebaceous cysts: conventional wide excision, minimal excision, and punch biopsy excision. A new method with two steps is proposed. First, a laser is used to make a small hole for removal of the content. Then the cyst wall is removed entirely with a minimal excision about 1 month later. With this method, the cyst is completely removed with only a small scar. It offers a good alternative for eradication of uninfected cysts, especially large cysts or cysts located in areas of thick skin or cosmetic concern

Explore postgraduate biomedical engineering course integration between medical signal processing and drug development: example for drug development in brain disease

Medical signal processing is a compulsory course in our university’s undergraduate biomedical engineering programme. Recently, application of medical signal processing in supporting new drug development has emerged as a promising strategy in neurosciences. Here, we discuss the curriculum reformation in biomedical signal processing course in the context of drug development and application in central nervous system, with a particular emphasis in knowledge integration

Specific frequency bands of amplitude low-frequency fluctuations in memory-related cognitive impairment: predicting Alzheimer’s disease

Resting-state functional magnetic resonance imaging was utilized to measure the amplitude low frequency fluctuations (ALFF) in human subjects with Alzheimer’s disease (AD) and normal control (NC). Two specific frequency bands (Slow5: 0.01-0.027Hz and Slow4: 0.027-0.073Hz) were analysed in the main cognitive control related four subregions of the right ventral lateral prefrontal cortex (VLPFC), i.e. IFJ, posterior-VLPFC, mid-VLPFC, and anterior-VLPFC. Differences in ALFF values between the AD and the NC group were found throughout the subregions of the right VLPFC. Compared to normal control group, decreased ALFF values were observed in AD patients in the IFJ (in two given frequency bands), and the mid-VLPFC (in Slow5). In contrast, increased ALFF valued were observed in AD patients in the posterior- and anterior-VLPFC (in both Slow5 and Slow4), and also in the mid-VLPFC in Slow4. Moreover, significant ALFF differences between the IFJ and three other subregions of the right VLPFC were found. Furthermore, ALFF values in the right VLPFC showed significant correlations with the time course of disease. Taken together, our findings suggest that AD patients have largely abnormalities in intrinsic neural oscillations which are in line with the AD pathophysiology, and further reveal that the abnormalities are dependent on specific frequency bands. Thus, frequency-domain analyses of the ALFF may provide a useful tool to investigate the AD pathophysiology

Determination of dezocine in rabbit plasma by liquid chromatography-mass spectrometry and its application

A sensitive and selective liquid chromatography-mass spectrometry (LC–MS) method for determination of dezocine in rabbit plasma was developed and validated. After addition of diazepam as internal standard (IS), liquid–liquid extraction (LLE) was used for sample preparation, and chromatography involved Agilent SB-C18 column (2.1 mmx50 mm, 3.5 um) using 0.1 % formic acid in water and acetonitrile as a mobile phase with gradient elution. Detection involved positive ion mode electrospray ionization (ESI), and selective ion monitoring (SIM) mode was used for quantification of target fragment ions m/z 245.8 for dezocine and m/z 284.8 for diazepam (internal standard, IS). The assay was linear over the range of 5–500 ng/mL for dezocine, with a lower limit of quantitation (LLOQ) of 5 ng/mL for dezocine. Intra- and inter-day precisions were less than 13 % and the accuracies were in the range of 93.1-105.2 % for dezocine. This developed method was successfully applied for the determination of dezocine in rabbit plasma for pharmacokinetic study.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively